Journal ArticleDOI
Analysis of the Complexity Measures in the EEG of Schizophrenia Patients
TLDR
The findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes and expects that nonlinear analysis will give us deeper understanding of schizophrenics' brain.Abstract:
Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.read more
Citations
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Journal ArticleDOI
Automated EEG-based screening of depression using deep convolutional neural network.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli,D. P. Subha +7 more
TL;DR: It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere, consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere.
Journal ArticleDOI
DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression
TL;DR: A novel EEG based computer-aided (CAD) Hybrid Neural Network that can be identified as DepHNN (Depression Hybrid Neural network) for depression screening is presented, which has attained an accuracy of 99.10% with mean absolute error (MAE) of 0.2040.
Journal ArticleDOI
Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal
TL;DR: Multivariate analysis of the EEG signal for the detection of Schizophrenia condition and five entropy measures measured from the IMF signal showed a significant difference.
Journal ArticleDOI
Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects
TL;DR: The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals.
Journal ArticleDOI
An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes.
TL;DR: The results verified the efficiency and reliability of the proposed system in predicting stress and non-stress on new patients, and showed that the proposed framework has compelling performance and can be employed for stress detection and evaluation in medical, educational and industrial fields.
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